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Abstract If Type Ia supernovae (SNe Ia) result from a white dwarf being ignited by Roche-lobe overflow from a nondegenerate companion, then as the SN explosion runs into the companion star its ejecta will be shocked, causing an early blue excess in the lightcurve. A handful of these excesses have been found in single-object studies, but inferences about the population of SNe Ia as a whole have been limited because of the rarity of multiwavelength follow-up within days of explosion. Here we present a 3 yr investigation yielding a nearly unbiased sample of nine nearby (z < 0.01) SNe Ia with exemplary early data. The data are multiwavelength, coveringUBVgriand Neil Gehrels Swift Observatory UV bandpasses, and also early, with an average first epoch 16.0 days before maximum light. Of the nine objects, three show early blue excesses. We do not find enough statistical evidence to reject the null hypothesis that SNe Ia predominantly arise from Roche-lobe-overflowing single-degenerate systems (p= 0.94). When looking at the objects’ colors, we find the objects are almost uniformly near-UV–blue, in contrast to earlier literature samples which found that only a third of SNe Ia are near-UV–blue, and we find a seemingly continuous range ofB − Vcolors in the days after explosion, again in contrast with earlier claims in the literature. This study highlights the importance of early, multiwavelength, high-cadence data in determining the progenitor systems of SNe Ia and in revealing their diverse early behavior. 

An increasing number of location-based service providers are taking the advantage of cloud computing by outsourcing their Point of Interest (POI) datasets and query services to third-party cloud service providers (CSPs), which answer various location-based queries from users on their behalf. A critical security challenge is to ensure the integrity and completeness of any query result returned by CSPs. As an important type of queries, a location-based skyline query (LBSQ) asks for the POIs not dominated by any other POI with respect to a given query position, i.e., no POI is both closer to the query position and more preferable with respect to a given numeric attribute. While there have been several recent attempts on authenticating outsourced LBSQ, none of them support the shortest path distance that is preferable to the Euclidian distance in metropolitan areas. In this paper, we tackle this open challenge by introducing AuthSkySP, a novel scheme for authenticating outsourced LBSQ under the shortest path distance, which allows the user to verify the integrity and completeness of any LBSQ result returned by an untrusted CSP. We confirm the effectiveness and efficiency of our proposed solution via detailed experimental studies using both real and synthetic datasets. 

IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, rendering them unusable in detecting compromised IoT devices. In this paper we develop an effective and efficient framework, named CUMAD, to detect compromised IoT devices. Instead of directly relying on individual anomalous events, CUMAD aims to accumulate sufficient evidence in detecting compromised IoT devices, by integrating an autoencoder-based anomaly detection subsystem with a sequential probability ratio test (SPRT)-based sequential hypothesis testing subsystem. CUMAD can effectively reduce the number of false alerts in detecting compromised IoT devices, and moreover, it can detect compromised IoT devices quickly. Our evaluation studies based on the public-domain N-BaIoT dataset show that CUMAD can on average reduce the false positive rate from about 3.57% using only the autoencoder-based anomaly detection scheme to about 0.5%; in addition, CUMAD can detect compromised IoT devices quickly, with less than 5 observations on average. 

IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, rendering them unusable in detecting compromised IoT devices. In this paper we develop an effective and efficient framework, named CUMAD, to detect compromised IoT devices. Instead of directly relying on individual anomalous events, CUMAD aims to accumulate sufficient evidence in detecting compromised IoT devices, by integrating an autoencoder-based anomaly detection subsystem with a sequential probability ratio test (SPRT)-based sequential hypothesis testing subsystem. CUMAD can effectively reduce the number of false alerts in detecting compromised IoT devices, and moreover, it can detect compromised IoT devices quickly. Our evaluation studies based on the public-domain N-BaIoT dataset show that CUMAD can on average reduce the false positive rate from about 3.57% using only the autoencoder-based anomaly detection scheme to about 0.5%; in addition, CUMAD can detect compromised IoT devices quickly, with less than 5 observations on average. 

We present a comprehensive photometric and spectroscopic study of the Type IIP supernova (SN) 2018is. TheVband luminosity and the expansion velocity at 50 days post-explosion are −15.1 ± 0.2 mag (corrected for A_V= 1.34 mag) and 1400 km s⁻¹, classifying it as a low-luminosity SN II. The recombination phase in theVband is shorter, lasting around 110 days, and exhibits a steeper decline (1.0 mag per 100 days) compared to most other low-luminosity SNe II. Additionally, the optical and near-infrared spectra display hydrogen emission lines that are strikingly narrow, even for this class. The Fe IIand Sc IIline velocities are at the lower end of the typical range for low-luminosity SNe II. Semi-analytical modelling of the bolometric light curve suggests an ejecta mass of ∼8 M_⊙, corresponding to a pre-supernova mass of ∼9.5 M_⊙, and an explosion energy of ∼0.40 × 10⁵¹erg. Hydrodynamical modelling further indicates that the progenitor had a zero-age main sequence mass of 9 M_⊙, coupled with a low explosion energy of 0.19 × 10⁵¹erg. The nebular spectrum reveals weak [O I]λλ6300,6364 lines, consistent with a moderate-mass progenitor, while features typical of Fe core-collapse events, such as He I, [C I], and Fe I, are indiscernible. However, the redder colours and low ratio of Ni to Fe abundance do not support an electron-capture scenario either. As a low-luminosity SN II with an atypically steep decline during the photospheric phase and remarkably narrow emission lines, SN 2018is contributes to the diversity observed within this population. 

Abstract Type Ibn supernovae (SNe Ibn) are rare stellar explosions powered primarily by interaction between the SN ejecta and H-poor, He-rich material lost by their progenitor stars. Multiwavelength observations, particularly in the X-rays, of SNe Ibn constrain their poorly understood progenitor channels and mass-loss mechanisms. Here we present Swift X-ray, ultraviolet, and ground-based optical observations of the Type Ibn SN 2022ablq, only the second SN Ibn with X-ray detections to date. While similar to the prototypical Type Ibn SN 2006jc in the optical, SN 2022ablq is roughly an order of magnitude more luminous in the X-rays, reaching unabsorbed luminositiesL_X∼ 4 × 10⁴⁰erg s⁻¹between 0.2–10 keV. From these X-ray observations we infer time-varying mass-loss rates between 0.05 and 0.5M_⊙yr⁻¹peaking 0.5–2 yr before explosion. This complex mass-loss history and circumstellar environment disfavor steady-state winds as the primary progenitor mass-loss mechanism. We also search for precursor emission from alternative mass-loss mechanisms, such as eruptive outbursts, in forced photometry during the 2 yr before explosion. We find no statistically significant detections brighter thanM≈ −14—too shallow to rule out precursor events similar to those observed for other SNe Ibn. Finally, numerical models of the explosion of an ∼15M_⊙helium star that undergoes an eruptive outburst ≈1.8 yr before explosion are consistent with the observed bolometric light curve. We conclude that our observations disfavor a Wolf–Rayet star progenitor losing He-rich material via stellar winds and instead favor lower-mass progenitor models, including Roche-lobe overflow in helium stars with compact binary companions or stars that undergo eruptive outbursts during late-stage nucleosynthesis stages.